The conventional wisdom in deep learning is that GPUs are essential compute tools for advanced neural networks. I’m always skeptical of conventional wisdom, but for BabbleLabs, it seems to hold true. The sheer performance of GPUs, combined with their robust support in deep learning programming environments, allows us to train bigger, more complex networks with vastly more data and deploy them commercially at low cost. GPUs are a key element in BabbleLabs’ delivery of the world’s best speech enhancement technology.
The deep learning computing model gives us powerful new tools for extracting fresh insights from masses of complex data — data that has long defied good systematic analysis with explicit algorithms. The model has already transformed vision and speech processing, obsoleting most conventional classification and generation methods in just the last five years. Deep learning is now being applied — often with spectacular results — across transportation, public safety, medicine, finance, marketing, manufacturing, and social media. This already makes it one of the most significant developments in computing in the past two decades. In time, we may rate its impact in the same category with the “superstars” of tech transformation — the emergence of high-speed Internet and smart mobile devices.
The promise of deep learning is matched with a curse: it demands huge data sets and correspondingly huge computing resources for successful training and use. For example, a single full training of BabbleLabs’ most advanced speech enhancement network requires between
1019 and 1020
floating point operations, using ...